The invention relates generally to the computerized, automated assessment of computed tomography (CT) scans (or images), and more particularly, to methods, systems, and computer program products for delineating the chest wall in helical CT scans of the thorax to assess pleural disease.
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; 6,011,862; 6,058,322; 6,067,373; 6,075,878; 6,078,680; 6,088,473; 6,112,112; 6,138,045; 6,141,437; 6,185,320; 6,205,348 as well as U.S. patent application Ser. Nos. 08/173,935; 08/398,307 (PCT Publication WO 96/27846); 08/536,149; 08/900,188; 08/900,189; 09/027,468; 09/028,518; 09/092,004; 09/121,719; 09/141,535; 09/471,088; 09/692,218; 09/716,335; 09/759,333; 09/760,854; 09/773,636; 09/816,217; 09/830,562; and 09/830,574 and PCT patent applications PCT/US00/41299; PCT/US01/00680; PCT/US01/01478 and PCT/US01/01479, all of which are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. patents and applications, as well as described in the references identified in the following LIST OF REFERENCES by the author(s) and year of publication and cross-referenced throughout the specification by reference to the respective number, in parentheses, of the reference:
1. Ng C S, Munden R F, Libshitz H I. Malignant pleural mesothelioma: The spectrum of manifestations on CT in 70 cases. Clinical Radiology 54:415-421, 1999.
2. Sterman D H, Kaiser L R, Albelda S M. Advances in the treatment of malignant pleural mesothelioma. Chest 116:504-520, 1999.
3. Huo Z, Giger M L, Vyborny C J, Bick U, Lu P, Wolverton D E, Schmidt R A. Analysis of spiculation in the computerized classification of mammographic masses. Medical Physics 22:1569-1579, 1995.
4. Jiang Y, Nishikawa R M, Wolverton D E, Metz C E, Giger M L, Schmidt R A, Vyborny C J, Doi K. Malignant and benign clustered microcalcifications: Automated feature analysis and classification. Radiology 201:581-582, 1996.
5. Giger M L, Doi K, MacMahon H, Nishikawa R M, Hoffmann K R, Vyborny C J, Schmidt R A, Jia H, Abe K, Chen X, Kano A, Katsuragawa S, Yin F -F, Alperin N, Metz C E, Behlen F M, Sluis D. An xe2x80x9cintelligentxe2x80x9d workstation for computer-aided diagnosis. RadioGraphics 13:647-656, 1993.
6. Xu X -W, Doi K, Kobayashi T, MacMahon H, Giger M L. Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Medical Physics 24:1395-1403, 1997.
7. Katsuragawa S, Doi K, MacMahon H, Monnier-Cholley L, Ishida T, Kobayashi T. Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks. Journal of Digital Imaging 10:108-114, 1997.
8. Difazio M C, MacMahon H, Xu X -W, Tsai P, Shiraishi J, Armato S G, III, Doi K. Digital chest radiography: Effect of temporal subtraction images on detection accuracy. Radiology 202:447-452, 1997.
9. Armato S G, III, Giger M L, MacMahon H. Automated detection of lung nodules in CT scans: Preliminary results. Medical Physics (in press), 2001.
10. Webb W R, Brant W E, Helms C A. Fundamentals of Body CT. Philadelphia, Pa.: W. B. Saunders Company; 1998.
11. Giger M L, Bae K T, MacMahon H. Computerized detection of pulmonary nodules in computed tomography images. Investigative Radiology 29:459-465, 1994.
12. Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision. Pacific Grove, Calif.: Brooks/Cole Publishing Company; 1999.
13. Armato S G, III, Giger M L, Moran C J, Doi K, MacMahon H. Computerized detection of lung nodules in computed tomography scans. In: K Doi, H MacMahon, M L Giger, and K R Hoffmann, eds. Computer-Aided Diagnosis in Medical Images. Amsterdam: Elsevier Science; 1999:119-123.
14. Armato S G, III, Giger M L, Blackburn J T, Doi K, MacMahon H. Three-dimensional approach to lung nodule detection in helical CT. SPIE Proceedings 3661:553-559, 1999.
15. Armato S G, III, Giger M L, Moran C J, MacMahon H, Doi K. Automated detection of pulmonary nodules in helical computed tomography images of the thorax. SPIE Proceedings 3338:916-919, 1998.
16. Fitzgibbon A W, Pilu M, Fisher R B. Direct least squares fitting of ellipses. In: eds. International Conference on Pattern Recognition. Vienna: IEEE Computer Society; 1996.
17. Mathews J H. Numerical Methods for Mathematics, Science, and Engineering. Englewood Cliffs, N.J.: Prentice Hall; 1992.
Malignant pleural mesothelioma is diagnosed in approximately 2000-3000 people in the Unites States each year (see Reference 1) and is associated with an extremely poor prognosis. Given the correlation of mesothelioma with asbestos exposure and a latency of up to 35-40 years (see Reference 1), the incidence of malignant mesothelioma is expected to rise over the next decade or two. Although numerous attempts to develop an accepted treatment for the management of mesothelioma patients have been largely unsuccessful, investigators continue to explore novel chemotherapy agents and multimodality treatment programs in an effort to reduce morbidity and, potentially, prolong the survival of patients afflicted with this disease (see Reference 2).
Computed tomography (CT) has been a major advance in the diagnosis and assessment of mesothelioma. Moreover, CT is an important tool for monitoring a patient""s response to treatment in a variety of clinical trials. The increased use of CT in the evaluation of mesothelioma demands new, computerized image analysis methodologies to facilitate extraction of the image features that are most relevant to the characterization of mesothelioma. Image processing and computer vision techniques have been developed for the detection and classification of breast masses and microcalcifications on mammograms (see References 3-5), for the detection of lung nodules and interstitial disease on chest radiographs (see References 6, 7), for the enhanced visualization of temporal change on sequential chest radiographs (see Reference 8), and for the detection of lung nodules in thoracic CT scans (see Reference 9). The evaluation of mesothelioma could benefit from similar techniques that would assist radiologists and clinicians in the reliable, consistent, and reproducible quantification of mesothelioma.
While currently no standard exists for radiologic measurement of mesothelioma, one protocol indicates manual measurement of up to three areas of the pleural rind at each of three levels (i.e., three separate CT sections). To accomplish this task, a radiologist holds a ruler up to the CT film, makes the appropriate measurement, and uses a scale printed on the film to convert the measurement of the image into the real-world size of the measured structure. More accurate and global assessment of mesothelioma certainly requires the acquisition of many more than nine measurements (i.e., three measurements on each of three CT sections).
However, the amount of effort required to accomplish this task with the current manual procedure places a practical limit on the number of measurements that may be acquired.
Accordingly, an object of this invention is to provide an improved method, system, and computer program product for the automated measurements of pleural space and pleural thickening in thoracic CT scans, including automated segmentation of lungs in thoracic CT scans, automated construction of a xe2x80x9cchest wall imagexe2x80x9d from thoracic CT scans, automated and semi-automated identification (segmentation) of ribs in thoracic CT scans, and automated delineation of the chest wall in thoracic CT scans.
These and other objects are achieved by way of a method, system, and computer program product constructed according to the present invention, wherein pleural disease (particularly mesothelioma, a pleura-based cancer) is assessed in thoracic CT scans acquired with either a standard helical protocol or a low-dose helical protocol by measuring pleural space and pleural thickening in the CT scans.
In particular, according to one aspect of the present invention, there is provided a novel method for assessing pleural disease, including the steps of obtaining an image including the pleural space and/or the pleural thickening, segmenting lungs in the obtained image, constructing a chest wall image from the obtained image using a lung boundary obtained in the segmenting step, identifying ribs in the chest wall image, mapping a location of the identified ribs back into the obtained image, and determining in the obtained image the extent of the pleural space and/or the pleural thickening between the identified ribs mapped back into the obtained CT image and at least one segmented lung.
According to another aspect of the present invention, there is provided a novel system implementing the method of the invention.
According to still another aspect of the present invention, there is provided a novel computer program product, included within a computer readable medium of a computer system, which upon execution causes the computer system to perform the method of the invention.